Virtual Storage-Based DSM With Error-Driven Prediction Modulation for Microgrids

Microgrids consider adjustable loads in demand-side management (DSM), which respond to dynamic market prices. A reliable DSM strategy relies on load forecasting techniques in day-ahead (DA) scheduling. This paper applies an error-driven prediction modulation to evaluate these differences. In addition, this paper creates two new DSM methods with an evaluation environment to utilize this modulation. The first method adds this modulation directly to traditional microgrid DSM with electrical storage. The second method creates two virtual sub-storages for behavior adjustment in both DA and real-time (RT) markets. The results of numerical studies indicate that the new DSM methods can reduce microgrid operation costs.

[1]  Volker J. Sorger,et al.  Optimization of Data Center Battery Storage Investments for Microgrid Cost Savings, Emissions Reduction, and Reliability Enhancement , 2016 .

[2]  Yitao Liu,et al.  Deep learning based ensemble approach for probabilistic wind power forecasting , 2017 .

[3]  Steven L. Horstmeyer Local Climatological Data Annual Summaries 2009 , 2011 .

[4]  Qinglin Wang,et al.  Event-Trigger Heterogeneous Nonlinear Filter for Wide-Area Measurement Systems in Power Grid , 2019, IEEE Transactions on Smart Grid.

[5]  Amin Khodaei,et al.  A Comprehensive Battery Energy Storage Optimal Sizing Model for Microgrid Applications , 2018, IEEE Transactions on Power Systems.

[6]  Yushi Miura,et al.  Stability Assessment and Optimization Methods for Microgrid With Multiple VSG Units , 2018, IEEE Transactions on Smart Grid.

[7]  Martin Reisslein,et al.  Integrating Renewable Energy Resources into the Smart Grid: Recent Developments in Information and Communication Technologies , 2018, IEEE Transactions on Industrial Informatics.

[8]  Junbo Zhao,et al.  Short-Term State Forecasting-Aided Method for Detection of Smart Grid General False Data Injection Attacks , 2017, IEEE Transactions on Smart Grid.

[9]  Tamás Keviczky,et al.  Probabilistic Energy Management for Building Climate Comfort in Smart Thermal Grids with Seasonal Storage Systems , 2016, IEEE Transactions on Smart Grid.

[10]  Khaled M. Abo-Al-Ez,et al.  A data mining based load forecasting strategy for smart electrical grids , 2016, Adv. Eng. Informatics.

[11]  Hoay Beng Gooi,et al.  Toward Optimal Energy Management of Microgrids via Robust Two-Stage Optimization , 2018, IEEE Transactions on Smart Grid.

[12]  Zhengtao Ding,et al.  Cooperative Optimal Control of Battery Energy Storage System Under Wind Uncertainties in a Microgrid , 2018, IEEE Transactions on Power Systems.

[13]  Mohammad Reza Aghamohammadi,et al.  A Three Stages Decision Tree-Based Intelligent Blackout Predictor for Power Systems Using Brittleness Indices , 2018, IEEE Transactions on Smart Grid.

[14]  Ioannis D. Schizas,et al.  Optimization-Based AC Microgrid Synchronization , 2017, IEEE Transactions on Industrial Informatics.

[15]  Ali Tajer,et al.  Load Forecasting via Diversified State Prediction in Multi-Area Power Networks , 2017, IEEE Transactions on Smart Grid.

[16]  Guidong Zhang,et al.  Power electronics converters: Past, present and future , 2018 .

[17]  Mohammad A. S. Masoum,et al.  An Adaptive Recursive Wavelet Based Algorithm for Real-Time Measurement of Power System Variables During Off-Nominal Frequency Conditions , 2018, IEEE Transactions on Industrial Informatics.

[18]  Bruno Sareni,et al.  Integrated Optimal Design of a Smart Microgrid With Storage , 2017, IEEE Transactions on Smart Grid.

[19]  Joongheon Kim,et al.  Residential Demand Response for Renewable Energy Resources in Smart Grid Systems , 2017, IEEE Transactions on Industrial Informatics.

[20]  Marcos J. Rider,et al.  Optimal Management of Energy Consumption and Comfort for Smart Buildings Operating in a Microgrid , 2019, IEEE Transactions on Smart Grid.

[21]  Xiantong Zhen,et al.  Multitarget Sparse Latent Regression , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Qing-Shan Jia,et al.  Performance Analysis and Comparison on Energy Storage Devices for Smart Building Energy Management , 2012, IEEE Transactions on Smart Grid.

[23]  Muralitharan Krishnan,et al.  Neural network based optimization approach for energy demand prediction in smart grid , 2018, Neurocomputing.

[24]  Qinghua Hu,et al.  Transfer learning for short-term wind speed prediction with deep neural networks , 2016 .

[25]  Xiaofei He,et al.  Multi-Target Regression via Robust Low-Rank Learning , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Yu Zhang,et al.  Robust Energy Management for Microgrids With High-Penetration Renewables , 2012, IEEE Transactions on Sustainable Energy.

[27]  Amin Khodaei,et al.  Leveraging Accuracy-Uncertainty Tradeoff in SVM to Achieve Highly Accurate Outage Predictions , 2018, IEEE Transactions on Power Systems.

[28]  Junyong Liu,et al.  Robust Energy Management of Microgrid With Uncertain Renewable Generation and Load , 2016, IEEE Transactions on Smart Grid.

[29]  Hoay Beng Gooi,et al.  Multi-Objective Optimal Dispatch of Microgrid Under Uncertainties via Interval Optimization , 2019, IEEE Transactions on Smart Grid.